ruvnet

Getting Started with ChatGPT Codex + Mastra Agents

Step-by-Step Instructions

1. Open the ChatGPT Codex task setup panel. This is where you configure your environment before starting a task.

2. Locate the "Setup Script" field. You’ll see a note that internet access is disabled after the script runs.

noobnooc

// Website you intended to retrieve for users.
const upstream = 'api.openai.com'

// Custom pathname for the upstream website.
const upstream_path = '/'

// Website you intended to retrieve for users using mobile devices.
const upstream_mobile = upstream

// Countries and regions where you wish to suspend your service.

progrium

Key Points

• In general, binaries built just for x86 architecture will automatically be run in x86 mode
• You can force apps in Rosetta 2 / x86 mode by right-clicking app, click Get Info, check "Open using Rosetta"
• You can force command-line apps by prefixing with arch -x86_64, for example arch -x86_64 go
• Running a shell in this mode means you don't have to prefix commands: arch -x86_64 zsh then go or whatever
• Don't just immediately install Homebrew as usual. It should most likely be installed in x86 mode.

Homebrew

Not all toolchains and libraries properly support M1 arm64 chips just yet. Although

bcnzer

addEventListener('fetch', event => {
  event.respondWith(handleRequest(event.request))
})

// Following code is a modified version of that found at https://blog.cloudflare.com/dronedeploy-and-cloudflare-workers/

/**
 * Fetch and log a request
 * @param {Request} request
 */

nathanmalishev

Parameters:
  Env:
    Description: An environment name that will be prefixed to resource names
    Type: String
    AllowedValues: ["development", "production"]

resources:
  NetworkRole:
    Type: "AWS::IAM::Role"
    Properties: 

nathanmalishev

Version: 2012-10-17
Statement:
  - Effect: Allow
    Action:
      - 'cloudformation:CreateChangeSet'
      - 'cloudformation:DescribeChangeSet'
      - 'cloudformation:ExecuteChangeSet'
      - 'cloudformation:DescribeStacks'
    Resource:
      - 'arn:aws:cloudformation:<region>:<account_no>:stack/<roles_permission_stack_name>/*'

groteworld

// An usage of AWS.IotData in Lambdas
//
// This example assumes some things
// 1. That you have a environment variable AWS_IOT_ENDPOINT. This is the url that you can find in AWS IoT dashboard settings
// 2. The lambda and your aws iot devices are on the same account and region

const AWS = require('aws-sdk');
const iotData = new AWS.IotData({ endpoint: process.env.AWS_IOT_ENDPOINT });

const handler = (event, context) => {

mjdietzx

#!/bin/bash

# creates a GitHub release (draft) and adds pre-built artifacts to the release
# after running this script user should manually check the release in GitHub, optionally edit it, and publish it

# args: :version_number (the version number of this release), :body (text describing the contents of the tag)
# example usage: ./gh_release_bamboo.sh "1.0.0" "Release notes: ..."
# => name: nRF5-ble-driver_<platform_name>_1.0.0_compiled-binaries.zip example: nRF5-ble-driver_win-64_2.0.1_compiled-binaries.zip

# to ensure that bash is used: https://answers.atlassian.com/questions/28625/making-a-bamboo-script-execute-using-binbash

kharandziuk

This article shows how to apply Node.js Stream and a bit of Reactive programming to a real(tm) problem. The article is intended to be highly practical and oriented for an intermediate reader. I intentionally omit some basic explanations. If you miss something try to check the API documentation or its retelling(e.g.: this one)

So, lets start from the problem description. We need to implement a simple web scraper which grabs all the data from some REST API, process the data somehow and inserts into our Database. For simplicity, I omit the details about the actual database and REST API(in real life it was the API of some travel fare aggregator website and a Pg database)

Consider we have two functions(code of the IO simulator functions and the other article code is here):

getAPI(n, count) // pseudo API ca

paulirish

A few conversations have circled around user-side structural profiling. For context, see React PR #7549: Show React events in the timeline when ReactPerf is active

One particular concern is the measurement overhead. This gist has a benchmarking script (measure.js) for evaluating overhead and initial results.

Results: performance.mark()

Runs about 0.65µs per mark() call. Naturally, that's ~= an overhead of 1ms for 1500 mark()s.